High-level human instructions often correspond to behaviors with multiple implicit steps. In order for robots to be useful in the real world, they must be able to to reason over both motions and intermediate goals implied by human instructions. In this work, we propose a framework for learning representations that convert from a natural-language command to a sequence of intermediate goals for execution on a robot. A key feature of this framework is prospection, training an agent not just to correctly execute the prescribed command, but to predict a horizon of consequences of an action before taking it. We demonstrate the fidelity of plans generated by our framework when interpreting real, crowd-sourced natural language commands for a robot in simulated scenes.
Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation
New York University（纽约大学）；NYU Langone Health（纽约大学兰格医学中心）；Genentech（基因泰克）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Wenzhou Medical University（温州医科大学）；Macau University of Science and Technology（澳门科学大学）
MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization
Hong Kong University of Science（香港科技大学）；Hong Kong Polytechnic University（香港理工大学）；Tsinghua University（清华大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots
Department of Cognitive Robotics, Delft University of Technology（认知机器人学系，代尔夫特理工大学）；Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology（计算机科学与人工智能实验室（CSAIL），麻省理工学院）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（机器人与机电研究所，德国航空航天中心（DLR））；Department of Industrial Engineering, University of Trento（工业工程系，特伦托大学）
